Note that this reference documentation is identical to the help that is displayed in MATLAB when you type “help ft_omri_align_init”.

  function model = ft_omri_align_init(Vr,flags)
  Ripped out of SPM 8 and modified (2010, S.Klanke)
  Estimation of within modality rigid body movement parameters
  FORMAT P = spm_realign(P,flags)
  P     - matrix of filenames {one string per row}
          All operations are performed relative to the first image.
          ie. Coregistration is to the first image, and resampling
          of images is into the space of the first image.
          For multiple sessions, P should be a cell array, where each
          cell should be a matrix of filenames.
  flags - a structure containing various options.  The fields are:
          quality - Quality versus speed trade-off.  Highest quality
                    (1) gives most precise results, whereas lower
                    qualities gives faster realignment.
                    The idea is that some voxels contribute little to
                    the estimation of the realignment parameters.
                    This parameter is involved in selecting the number
                    of voxels that are used.
          fwhm    - The FWHM of the Gaussian smoothing kernel (mm)
                    applied to the images before estimating the
                    realignment parameters.
          sep     - the default separation (mm) to sample the images.
          PW      - a filename of a weighting image (reciprocal of
                    standard deviation).  If field does not exist, then
                    no weighting is done.
          interp  - B-spline degree used for interpolation
  A series of *.img conforming to SPM data format (see 'Data Format').
  If no output argument, then an updated voxel to world matrix is written
  to the headers of the images (a .mat file is created for 4D images).
  The details of the transformation are displayed in the
  results window as plots of translation and rotation.
  A set of realignment parameters are saved for each session, named:
  The voxel to world mappings.
  These are simply 4x4 affine transformation matrices represented in the
  NIFTI headers (see ).
  These are normally modified by the `realignment' and `coregistration'
  modules.  What these matrixes represent is a mapping from
  the voxel coordinates (x0,y0,z0) (where the first voxel is at coordinate
  (1,1,1)), to coordinates in millimeters (x1,y1,z1).
  x1 = M(1,1)*x0 + M(1,2)*y0 + M(1,3)*z0 + M(1,4)
  y1 = M(2,1)*x0 + M(2,2)*y0 + M(2,3)*z0 + M(2,4)
  z1 = M(3,1)*x0 + M(3,2)*y0 + M(3,3)*z0 + M(3,4)
  Assuming that image1 has a transformation matrix M1, and image2 has a
  transformation matrix M2, the mapping from image1 to image2 is: M2\M1
  (ie. from the coordinate system of image1 into millimeters, followed
  by a mapping from millimeters into the space of image2).
  These matrices allow several realignment or coregistration steps to be
  combined into a single operation (without the necessity of resampling the
  images several times).  The `.mat' files are also used by the spatial
  normalisation module.
  Friston KJ, Ashburner J, Frith CD, Poline J-B, Heather JD & Frackowiak
  RSJ (1995) Spatial registration and normalization of images Hum. Brain
  Map. 2:165-189
  Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging